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https://doi.org/10.1016/j.giq.2016.07.006

Abstract

E-petitioning technology platforms elicit the participation of citizens in the policy-making process but at the same time create large volumes of unstructured textual data that are difficult to analyze. Fortunately, computational tools can assist policy analysts in uncovering latent patterns from these large textual datasets. This study uses such computational tools to explore e-petitions, viewing them as persuasive texts with linguistic and semantic features that may be related to the popularity of petitions, as indexed by the number of signatures they attract. Using We the People website data, we analyzed linguistic features, such as extremity and repetition, and semantic features, such as named entities and topics, to determine whether and to what extent they are related to petition popularity. The results show that each block of variables independently explains statistically significant variation in signature accumulation, and that 1) language extremity is persistently and negatively associated with petition popularity, 2) petitions with many names tend not to become popular, and 3) petition popularity is associated with petitions that include topics familiar to the public or about important social events. We believe explorations along these lines will yield useful strategies to address the wicked problem of too much text data and to facilitate the enhancement of public participation in policy-making.